Diabetes Risk Among Overweight and Obese Metabolically Healthy Young Adults
Bibliographic record
Abstract
OBJECTIVE: To determine diabetes incidence over time among obese young adults without metabolic risk factors. RESEARCH DESIGN AND METHODS: Incident diabetes during a median follow-up of 6.1 years was assessed among 33,939 young men (mean age 30.9 ± 5.2 years) of the Metabolic, Lifestyle and Nutrition Assessment in Young Adults cohort who were stratified for BMI and the number of metabolic abnormalities (based on the Adult Treatment Panel-III). Metabolically healthy (MH) obesity was defined as BMI ≥30 kg/m2 in the presence of normoglycemia, normal blood pressure, and normal levels of fasting triglyceride and HDL-cholesterol levels (n = 631). RESULTS: A total of 734 new cases of diabetes were diagnosed during 210,282 person-years of follow-up. The incidence rate of diabetes among participants with no metabolic risk factors was 1.15, 2.10, and 4.34 cases per 1,000 person-years among lean, overweight, and obese participants, respectively. In a multivariable model adjusted for age, region of origin, family history of diabetes, physical activity, fasting plasma glucose, triglyceride level, HDL-cholesterol, systolic blood pressure, and white blood cell count, a higher diabetes risk was observed among MH-overweight (hazard ratio [HR] 1.89 [95% CI 1.25-2.86]; P < 0.001) and MH-obese (HR 3.88 [95% CI 1.94-7.77]; P < 0.001) compared with MH-normal weight subjects. There was no interaction between BMI and the number of metabolic abnormalities at enrollment in predicting diabetes risk. CONCLUSIONS: Healthy metabolic profile and the absence of diabetes risk factors do not protect young adults from incident diabetes associated with overweight and obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".